Review of relevant variables for the sample of caregivers
Date: 2017-05-24
# Attach these packages so their functions don't need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
library(magrittr) # enables piping : %>%
library(dplyr)
library(TabularManifest)# Call `base::source()` on any repo file that defines functions needed below. Ideally, no real operations are performed.
source("./scripts/common-functions.R") # used in multiple reports
source("./scripts/graphing/graph-presets.R") # fonts, colors, themes
source("./scripts/graphing/graph-elemental.R") # graphs to be used in dipslays
source("./scripts/graphing/graph-complex.R") # info displays
# Verify these packages are available on the machine, but their functions need to be qualified: http://r-pkgs.had.co.nz/namespace.html#search-path
requireNamespace("ggplot2") # graphing
# requireNamespace("readr") # data input
requireNamespace("tidyr") # data manipulation
requireNamespace("dplyr") # Avoid attaching dplyr, b/c its function names conflict with a lot of packages (esp base, stats, and plyr).
requireNamespace("testit")# For asserting conditions meet expected patterns.
# requireNamespace("car") # For it's `recode()` function.Exposition
path_input <- "./data-unshared/derived/2-dto_c.rds" # product of ./manipulation/2-identify-caregivers.R
# path_output <- ""#Put code in here. It doesn't call a chunk in the codebehind file.# list static variables
variables_static <- c(
"id" #
,"male" # Gender
,"birth_year" # Birth year from RAND longitudinal file
,"birth_month" # Month of birth
,"race" # Race
,"hispanic" # Whether Hispanic
,"cohort" # Cohort based on birth yr
,"edu_years" # Years of Education
,"highest_degree" # Highest Degree
) # static
variables_longitudinal <- c(
"lb_wave" # Leave-behind wave
,"year" # Year
,"lb_65_wave" # Leave-behind wave at age 65 or older
,"hrs_tscore"
,"interview_date" # Interview data year and month
,"responded" #
,"proxy" #
,"hhres" #
,"countb20r" #
,"shhidpnr" #
,"rmaritalst" #
,"intage_r" #
,"rpartst" #
,"score_loneliness_3" #
,"score_loneliness_11" #
,"snspouse" #
,"snchild" #
,"snfamily" #
,"snfriends" #
,"socialnetwork_total" #
,"close_social_network" #
,"social_support_mean" #
,"social_strain_mean" #
,"social_contact_total" #
,"activity_mean" #
,"activity_sum" #
,"srmemory" #
,"srmemoryp" #
,"wrectoti" #
,"wrectotd" #
,"listassi"
,"mentalstatus_tot" #
,"vocab_total" #
,"dep_total" #
,"healthcond" #
,"exercise" #
,"memry"
,"memryq"
,"smemry"
,"smemryq"
,"memrye"
,"smemrye"
,"memryf"
,"smemryf"
,"alzhe"
,"salzhe"
,"alzheq"
,"salzheq"
,"alzhee"
,"salzhee"
,"alzhflag"
,"salzhflag"
,"demen"
,"sdemen"
,"demenq"
,"sdemenq"
,"demene"
,"sdemene"
,"demenflag"
,"sdemenflag"
,"dementia_ever"
,"alzheimer_ever"
,"memoryproblems_ever"
,"memory_disease_ever"
,"spouse_dementia_ever"
,"spouse_alzheimer_ever"
,"spouse_memoryproblems_ever"
,"spouse_memory_disease_ever"
, "spouse_memory_disease"
,"spouse_memory_disease_pattern"
) # not staticData
# load the product of 0-ellis-island.R, a list object containing data and metadata
dto <- readRDS(path_input)# dto %>% glimpse()
class(dto)[1] "tbl_df" "tbl" "data.frame"#str(dto)# rename variables for graphing convenience, Cassandra, please move upstream when stable
ds <- dto %>%
dplyr::rename_(
"id" = "id"
, "male" = "male"
, "birth_year" = "birthyr_rand"
, "birth_month" = "birthmo_rand"
, "race" = "race_rand"
, "hispanic" = "hispanic_rand"
, "cohort" = "cohort"
, "edu_years" = "raedyrs"
, "highest_degree" = "raedegrm"
)
# subset variables of relevance for this project
ds <- ds %>%
dplyr::select_(.dots = c(variables_static, variables_longitudinal)) %>%
as.data.frame() %>%
dplyr::mutate(
male = factor(male, levels = c(1,2), labels = c("Man", "Woman"))
,race = factor(race, levels = c(1, 2, 3), labels = c("White","Black","Other") )
,cohort = factor(cohort, levels = c(0, 1, 2, 3, 4, 5, 6), labels = c("Not in any cohort", "Ahead", "Coda", "Hrs", "WarBabies", "Early BabyBoomers", "Mid BabyBoomers") )
,age_at_visit = intage_r
,date_at_visit = interview_date
) %>%
tibble::as_tibble()
ds %>% glimpse(width = 105)Observations: 6,954
Variables: 81
$ id <dbl> 3020, 3020, 3020, 3020, 3020, 3020, 10281020, 10281020, 102810...
$ male <fctr> Woman, Woman, Woman, Woman, Woman, Woman, Man, Man, Man, Man,...
$ birth_year <dbl> 1938, 1938, 1938, 1938, 1938, 1938, 1934, 1934, 1934, 1934, 19...
$ birth_month <dbl> 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 6, 6, 6,...
$ race <fctr> White, White, White, White, White, White, Black, Black, Black...
$ hispanic <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ cohort <fctr> Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, Hrs, H...
$ edu_years <dbl> 16, 16, 16, 16, 16, 16, 11, 11, 11, 11, 11, 11, 17, 17, 17, 17...
$ highest_degree <dbl> 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 2, 2, 2,...
$ lb_wave <int> NA, 1, NA, 2, NA, 3, NA, NA, NA, NA, 1, NA, NA, NA, 1, NA, 2, ...
$ year <fctr> 2004, 2006, 2008, 2010, 2012, 2014, 2004, 2006, 2008, 2010, 2...
$ lb_65_wave <int> NA, 1, NA, 2, NA, 3, NA, NA, NA, NA, 1, NA, NA, NA, 1, NA, 2, ...
$ hrs_tscore <dbl> NA, 1.666667, 1.916667, 2.583333, 1.833333, 1.833333, NA, 1.75...
$ interview_date <dbl> 2004.500, 2006.167, 2008.083, 2010.667, 2012.500, 2014.333, 20...
$ responded <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
$ proxy <dbl> 0, 0, 0, 0, 0, 0, 1, 1, NaN, NaN, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ hhres <dbl> 2, 2, 2, 2, 2, 2, 2, 2, NaN, NaN, 2, 2, 3, 2, 2, 2, 2, 1, 2, 2...
$ countb20r <dbl> 2, 2, 2, 2, 2, NA, NaN, NaN, NaN, NaN, 2, NA, 2, 2, 2, 2, 2, N...
$ shhidpnr <dbl> 3010, 3010, 3010, 3010, 3010, 0, 10281010, 10281010, NaN, NaN,...
$ rmaritalst <dbl> 1, 1, 1, 1, 1, 7, 1, 1, NaN, NaN, 1, 1, 1, 1, 1, 1, 1, 7, 1, 1...
$ intage_r <dbl> 65, 67, 69, 72, 73, 75, 69, 71, NaN, NaN, 78, 79, 63, 65, 67, ...
$ rpartst <dbl> 0, 0, 0, 0, 0, 0, 0, 0, NaN, NaN, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
$ score_loneliness_3 <dbl> NaN, 1.333333, NaN, 1.000000, NaN, 1.666667, NaN, NaN, NA, NA,...
$ score_loneliness_11 <dbl> NA, NA, NA, 1.000000, NA, 1.363636, NA, NA, NA, NA, 1.818182, ...
$ snspouse <dbl> NaN, 1, NaN, 1, NaN, 0, NaN, NaN, NA, NA, 0, NaN, NaN, NaN, 1,...
$ snchild <dbl> NaN, 1, NaN, 1, NaN, 1, NaN, NaN, NA, NA, NaN, NaN, NaN, NaN, ...
$ snfamily <dbl> NaN, 1, NaN, 1, NaN, 1, NaN, NaN, NA, NA, 0, NaN, NaN, NaN, 1,...
$ snfriends <dbl> NaN, 1, NaN, 1, NaN, 1, NaN, NaN, NA, NA, 1, NaN, NaN, NaN, 1,...
$ socialnetwork_total <dbl> NA, 4, NA, 4, NA, 3, NA, NA, NA, NA, 1, NA, NA, NA, 4, NA, 4, ...
$ close_social_network <dbl> 0, 11, 0, 12, 0, 17, 0, 0, 0, 0, 8, 0, 0, 0, 5, 0, 8, 0, 0, 6,...
$ social_support_mean <dbl> NaN, 9.000000, NaN, 7.750000, NaN, 9.333333, NaN, NaN, NaN, Na...
$ social_strain_mean <dbl> NaN, 4.500000, NaN, 5.250000, NaN, 3.750000, NaN, NaN, NaN, Na...
$ social_contact_total <dbl> NA, 38, NA, 32, NA, 35, NA, NA, NA, NA, 13, NA, NA, NA, 28, NA...
$ activity_mean <dbl> NA, NA, NaN, 2.75, NaN, 2.25, NA, NA, NA, NA, 2.00, NaN, NA, N...
$ activity_sum <dbl> NA, NA, NaN, 55, NaN, 45, NA, NA, NA, NA, 40, NaN, NA, NA, NA,...
$ srmemory <dbl> 2, 2, 3, 3, 3, 3, NaN, NaN, NA, NA, 5, 5, 3, 3, 3, 3, 3, 3, 2,...
$ srmemoryp <dbl> 2, 1, 1, 2, 2, 2, NaN, NaN, NA, NA, 3, 3, 2, 2, 2, 2, 2, 2, 2,...
$ wrectoti <dbl> 6, 5, 5, 6, 5, 5, NaN, NaN, NA, NA, 1, 1, 7, 6, 5, 6, 5, 6, 6,...
$ wrectotd <dbl> 6, 6, 5, 4, 3, 5, NaN, NaN, NA, NA, 0, 0, 6, 5, 4, 5, 5, 4, 4,...
$ listassi <dbl> 21, 11, 1, 31, 21, 11, NaN, 31, NA, NA, 21, 31, 11, 31, 1, 21,...
$ mentalstatus_tot <dbl> 9, 9, 9, 9, 7, 8, NaN, NaN, NA, NA, NA, NA, NaN, 9, 9, 9, 9, 8...
$ vocab_total <dbl> NaN, 9, NaN, 7, NaN, NaN, NaN, NaN, NA, NA, NaN, NaN, NaN, 9, ...
$ dep_total <dbl> 0, 0, 0, 0, 1, 0, NaN, NaN, NA, NA, 1, 4, 0, 0, 0, 0, 0, 1, 1,...
$ healthcond <dbl> 2, 2, 2, 2, 4, 4, 2, 1, NA, NA, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, ...
$ exercise <dbl> 12, 13, 12, 11, 4, 3, 10, 7, NA, NA, 6, 8, 12, 12, 12, 12, 12,...
$ memry <dbl> 0, 0, 0, NA, NA, NA, 0, 0, NaN, NA, NA, NA, 0, 0, 0, NA, NA, N...
$ memryq <dbl> 2, 2, 2, NA, NA, NA, 2, 2, NaN, NA, NA, NA, 2, 2, 2, NA, NA, N...
$ smemry <dbl> 0, 0, 0, NA, NA, NA, 0, 0, NaN, NA, NA, NA, 0, 0, 0, NA, NA, N...
$ smemryq <dbl> 2, 2, 2, NA, NA, NA, 2, 2, NaN, NA, NA, NA, 2, 2, 2, NA, NA, N...
$ memrye <dbl> 0, 0, 0, NA, NA, NA, 0, 0, NaN, NA, NA, NA, 0, 0, 0, NA, NA, N...
$ smemrye <dbl> 0, 0, 0, NA, NA, NA, 0, 0, NaN, NA, NA, NA, 0, 0, 0, NA, NA, N...
$ memryf <dbl> 0, 0, NA, NA, NA, NA, 0, 0, NA, NA, NA, NA, 0, 0, NA, NA, NA, ...
$ smemryf <dbl> 0, 0, 0, NA, NA, NA, 0, 0, NaN, NA, NA, NA, 0, 0, 0, NA, NA, N...
$ alzhe <dbl> NA, NA, NA, 0, 0, 0, NA, NA, NA, NaN, 0, 0, NA, NA, NA, 0, 0, ...
$ salzhe <dbl> NA, NA, NA, 1, 1, NaN, NA, NA, NA, NaN, 0, 1, NA, NA, NA, 0, 0...
$ alzheq <dbl> NA, NA, NA, 2, 2, 2, NA, NA, NA, NaN, 5, 2, NA, NA, NA, 2, 2, ...
$ salzheq <dbl> NA, NA, NA, 2, 3, NaN, NA, NA, NA, NaN, 2, 5, NA, NA, NA, 2, 2...
$ alzhee <dbl> NA, NA, NA, 0, 0, 0, NA, NA, NA, NaN, 0, 0, NA, NA, NA, 0, 0, ...
$ salzhee <dbl> NA, NA, NA, 1, 1, NaN, NA, NA, NA, NaN, 0, 1, NA, NA, NA, 0, 0...
$ alzhflag <dbl> NA, NA, NA, NA, 0, 0, NA, NA, NA, NA, 0, 0, NA, NA, NA, NA, 0,...
$ salzhflag <dbl> NA, NA, NA, NA, 0, NaN, NA, NA, NA, NA, 0, 0, NA, NA, NA, NA, ...
$ demen <dbl> NA, NA, NA, 0, 0, 0, NA, NA, NA, NaN, 0, 0, NA, NA, NA, 0, 0, ...
$ sdemen <dbl> NA, NA, NA, NaN, NaN, NaN, NA, NA, NA, NaN, 0, NaN, NA, NA, NA...
$ demenq <dbl> NA, NA, NA, 2, 2, 2, NA, NA, NA, NaN, 5, 2, NA, NA, NA, 2, 2, ...
$ sdemenq <dbl> NA, NA, NA, 2, 2, NaN, NA, NA, NA, NaN, 2, 5, NA, NA, NA, 2, 2...
$ demene <dbl> NA, NA, NA, 0, 0, 0, NA, NA, NA, NaN, 0, 0, NA, NA, NA, 0, 0, ...
$ sdemene <dbl> NA, NA, NA, NaN, NaN, NaN, NA, NA, NA, NaN, 0, NaN, NA, NA, NA...
$ demenflag <dbl> NA, NA, NA, NA, 0, 0, NA, NA, NA, NA, 0, 0, NA, NA, NA, NA, 0,...
$ sdemenflag <dbl> NA, NA, NA, NA, 0, NaN, NA, NA, NA, NA, 0, 0, NA, NA, NA, NA, ...
$ dementia_ever <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,...
$ alzheimer_ever <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,...
$ memoryproblems_ever <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,...
$ memory_disease_ever <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,...
$ spouse_dementia_ever <lgl> NA, NA, NA, NA, NA, NA, FALSE, FALSE, FALSE, FALSE, FALSE, FAL...
$ spouse_alzheimer_ever <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR...
$ spouse_memoryproblems_ever <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,...
$ spouse_memory_disease_ever <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR...
$ spouse_memory_disease <dbl> 0, 0, 0, 1, 1, NA, 0, 0, NA, NA, 0, 1, 0, 0, 0, 0, 1, NA, 0, 0...
$ spouse_memory_disease_pattern <chr> "00011NA", "00011NA", "00011NA", "00011NA", "00011NA", "00011N...
$ age_at_visit <dbl> 65, 67, 69, 72, 73, 75, 69, 71, NaN, NaN, 78, 79, 63, 65, 67, ...
$ date_at_visit <dbl> 2004.500, 2006.167, 2008.083, 2010.667, 2012.500, 2014.333, 20...ds %>% names_labels() name label
1 id hhidpn: hhold id + person number /num
2 male <NA>
3 birth_year <NA>
4 birth_month <NA>
5 race <NA>
6 hispanic <NA>
7 cohort <NA>
8 edu_years <NA>
9 highest_degree <NA>
10 lb_wave <NA>
11 year <NA>
12 lb_65_wave <NA>
13 hrs_tscore <NA>
14 interview_date <NA>
15 responded inw7: =1 if respondent w7
16 proxy r7proxy:w7 whether proxy interview
17 hhres h7hhres:w7 number of people in hh
18 countb20r r7bwc20: w7 backwards count from 20
19 shhidpnr s7hhidpn:w7 spouse hhidpn
20 rmaritalst r7mstat:w7 r marital status
21 intage_r r7agey_e:w7 r age (years) at ivw endmon
22 rpartst r7mpart:w7 r partnered
23 score_loneliness_3 <NA>
24 score_loneliness_11 <NA>
25 snspouse Q7. LIVE WITH SPOUSE OR PARTNER
26 snchild Q10. HAVE ANY CHILDREN
27 snfamily Q14. HAVE ANY OTHER IMMEDIATE FAMILY
28 snfriends Q18. HAVE ANY FRIENDS
29 socialnetwork_total <NA>
30 close_social_network <NA>
31 social_support_mean <NA>
32 social_strain_mean <NA>
33 social_contact_total <NA>
34 activity_mean <NA>
35 activity_sum <NA>
36 srmemory RATE MEMORY
37 srmemoryp RATE MEMORY PAST
38 wrectoti NUMBER GOOD - IMMEDIATE
39 wrectotd NUMBER GOOD - DELAYED
40 listassi D104 WORD LIST ASSIGNMENT
41 mentalstatus_tot <NA>
42 vocab_total <NA>
43 dep_total <NA>
44 healthcond <NA>
45 exercise <NA>
46 memry r7memry:w7 r reports memory prob this wv
47 memryq r7memryq:w7 q-wording memory prob this wv
48 smemry s7memry:w7 s reports memory prob this wv
49 smemryq s7memryq:w7 q-wording memory prob this wv
50 memrye r7memrye:w7 r ever had memory problem
51 smemrye s7memrye:w7 s ever had memory problem
52 memryf r7memryf:w7 flag chg prev memory prob
53 smemryf s7memryf:w7 flag chg prev memory prob
54 alzhe r10alzhe:w10 r reports alzheimer this wv
55 salzhe s10alzhe:w10 s reports alzheimer this wv
56 alzheq r10alzheq:w10 q-wording of alzheimer this wv
57 salzheq s10alzheq:w10 q-wording of alzheimer this wv
58 alzhee r10alzhee:w10 r ever reported alzheimer
59 salzhee s10alzhee:w10 s ever reported alzheimer
60 alzhflag r11alzhef:w11 flag chg prev report of alzheimer
61 salzhflag s11alzhef:w11 flag chg prev report of alzheimer
62 demen r10demen:w10 r reports dementia this wv
63 sdemen s10demen:w10 s reports dementia this wv
64 demenq r10demenq:w10 q-wording of dementia this wv
65 sdemenq s10demenq:w10 q-wording of dementia this wv
66 demene r10demene:w10 r ever reported dementia
67 sdemene s10demene:w10 s ever reported dementia
68 demenflag r11demenf:w11 flag chg prev report of dementia
69 sdemenflag s11demenf:w11 flag chg prev report of dementia
70 dementia_ever <NA>
71 alzheimer_ever <NA>
72 memoryproblems_ever <NA>
73 memory_disease_ever <NA>
74 spouse_dementia_ever <NA>
75 spouse_alzheimer_ever <NA>
76 spouse_memoryproblems_ever <NA>
77 spouse_memory_disease_ever <NA>
78 spouse_memory_disease <NA>
79 spouse_memory_disease_pattern <NA>
80 age_at_visit r7agey_e:w7 r age (years) at ivw endmon
81 date_at_visit <NA>#How many individuals whose spouses have been diagnosed with memory disease are there?
ds %>% distinct(id) %>% count() # n = 1159, original n = 37495# A tibble: 1 × 1
n
<int>
1 1159#When were the spouses diagnosed?
#From 2004 to 2008 the question was whether a doctor had ever diagnosed a "memory problem"
table(ds$smemry, ds$year)
2004 2006 2008 2010 2012 2014
0 658 527 362 0 0 0
1 199 188 415 0 0 0
4 0 0 3 0 0 0#2010 question was "Has a doctor ever told you that you have Alzheimer's Disease?"
#In follow up waves participants were asked to confirm the diagnosis if given at a previous wave.
table(ds$salzhe, ds$year)
2004 2006 2008 2010 2012 2014
0 0 0 0 587 457 323
1 0 0 0 123 121 116
3 0 0 0 0 1 0
4 0 0 0 0 2 5#2010 question was:
#Has a doctor ever told you that you have dementia, senility or any other serious memory impairment?"
table(ds$sdemen, ds$year)
2004 2006 2008 2010 2012 2014
0 0 0 0 424 278 134
1 0 0 0 167 195 197
3 0 0 0 0 1 2
4 0 0 0 0 7 5#Display the flag variable patterns
table(ds$smemryf,ds$year)
2004 2006 2008 2010 2012 2014
0 967 871 738 0 0 0
1 7 22 12 0 0 0
2 36 24 28 0 0 0
3 0 0 3 0 0 0table(ds$salzhflag, ds$year)
2004 2006 2008 2010 2012 2014
0 0 0 0 0 589 447
1 0 0 0 0 4 3
2 0 0 0 0 0 1
3 0 0 0 0 2 5
6 0 0 0 0 6 0
8 0 0 0 0 1 0table(ds$sdemenflag, ds$year)
2004 2006 2008 2010 2012 2014
0 0 0 0 0 573 431
1 0 0 0 0 6 6
2 0 0 0 0 7 12
3 0 0 0 0 7 5
6 0 0 0 0 8 0
8 0 0 0 0 1 2table(ds$spouse_memory_disease, ds$year)
2004 2006 2008 2010 2012 2014
0 658 527 362 426 275 134
1 199 188 418 290 326 321#Display patterns of spouses diagnosis of memory problems with frequency below
table(ds$spouse_memory_disease_pattern)
000000 000001 00000NA 000010 000011 00001NA 0000NA0 0000NA1 0000NANA
42 510 6 18 330 198 6 30 30
000100 000101 00010NA 000110 000111 00011NA 0001NA0 0001NANA 000NA01
24 6 24 6 156 192 12 228 18
000NA11 000NA1NA 000NANA1 000NANANA 001000 001001 00100NA 001010 001011
12 12 6 18 102 12 12 6 12
00101NA 0010NA0 0010NANA 00110NA 001110 001111 00111NA 0011NANA 001NA00
36 12 48 6 18 72 102 114 6
001NA0NA 001NA11 001NA1NA 001NANA1 001NANANA 00NA001 00NA011 00NA01NA 00NA11NA
6 6 12 6 300 6 6 6 6
00NA1NANA 00NANA01 00NANA1NA 00NANANA1 00NANANANA 010000 010001 01000NA 010NANANA
24 12 6 6 48 18 6 6 6
011000 011001 01100NA 0110NANA 011100 011110 011111 01111NA 0111NANA
96 18 18 24 6 6 54 42 72
011NA0NA 011NA11 011NANANA 01NA000 01NA11NA 01NANA00 01NANANANA 0NA001NA 0NA0110
6 6 156 12 6 12 306 18 6
0NA011NA 0NA01NANA 0NA100NA 0NA1NANANA 0NANA011 0NANA100 0NANA111 0NANA1NANA 0NANANA01
6 6 12 12 6 6 12 18 6
0NANANA10 0NANANANA1 0NANANANANA 100000 1000NANA 100111 101111 10NANANANA 111000
6 6 60 6 12 6 6 30 6
111011 1111NANA 111NANANA 11NANANANA 1NA0000 1NA001NA 1NA0111 1NA1000 1NA100NA
6 6 18 66 6 6 6 174 42
1NA1011 1NA10NANA 1NA1100 1NA1101 1NA1111 1NA111NA 1NA11NANA 1NA1NA00 1NA1NANANA
12 36 6 6 30 30 54 6 114
1NANA000 1NANA111 1NANANA00 1NANANANANA NA00000 NA00001 NA0000NA NA00011 NA000NANA
12 6 6 480 12 24 6 18 18
NA00111 NA0011NA NA001NANA NA00NA1NA NA01000 NA0100NA NA0111NA NA011NANA NA01NANANA
12 12 6 6 6 12 6 12 42
NA0NA1NANA NA0NANA11 NA0NANANANA NA11000 NA1101NA NA11111 NA1111NA NA111NANA NA11NANANA
6 6 6 6 6 6 6 6 30
NA1NANA0NA NA1NANANANA NANA0000 NANA001NA NANA00NANA NANA011NA NANA01NANA NANA0NA0NA NANA1000
6 84 6 6 6 24 12 6 90
NANA100NA NANA1010 NANA1011 NANA10NA1 NANA10NANA NANA1111 NANA111NA NANA11NANA NANA1NANANA
30 6 6 6 48 54 12 18 132
NANANA001 NANANA00NA NANANA011 NANANA01NA NANANA0NA1 NANANA0NANA NANANA100 NANANA101 NANANA10NA
114 12 90 6 36 6 12 6 6
NANANA110 NANANA111 NANANA11NA NANANA1NANA NANANANA00 NANANANA01 NANANANA11 NANANANA1NA NANANANANA0
6 78 30 24 6 6 30 42 12
NANANANANA1 NANANANANANA
36 432 length(unique(ds$spouse_memory_disease_pattern))[1] 173unique(ds$spouse_memory_disease_pattern) [1] "00011NA" "00NANA01" "00001NA" "000000" "000111" "000001" "1NANANANANA"
[8] "NANANANANANA" "00NA11NA" "001000" "0000NANA" "NANA1000" "NANANA001" "1NA111NA"
[15] "000011" "NANA1NANANA" "00111NA" "NANA1111" "00NANANANA" "000100" "001NANANA"
[22] "00100NA" "NA1NANA0NA" "0010NANA" "0011NANA" "NA0111NA" "00010NA" "011111"
[29] "NA0000NA" "1NA1000" "NA0011NA" "011NANANA" "01NANA00" "NANA10NANA" "00NANA1NA"
[36] "NANA100NA" "001NA0NA" "0NANANANANA" "NANANANA1NA" "011110" "00NA1NANA" "010000"
[43] "1NA100NA" "0111NANA" "0001NANA" "NA011NANA" "NA111NANA" "01NANANANA" "NANANA0NA1"
[50] "NANA011NA" "NA00NA1NA" "00101NA" "101111" "0000NA1" "NA11NANANA" "NANANA011"
[57] "001001" "10NANANANA" "0000NA0" "NANANANA00" "NANANANANA0" "111011" "0NANANA10"
[64] "NA00001" "NA00111" "NANA111NA" "NANA10NA1" "01111NA" "NA00011" "0NANA1NANA"
[71] "NA0100NA" "000NA1NA" "01100NA" "000NANANA" "11NANANANA" "001NA11" "0110NANA"
[78] "00NANANA1" "NA01NANANA" "1NA11NANA" "1NA0111" "1NA1NANANA" "00000NA" "NA000NANA"
[85] "NA001NANA" "NANANANA11" "1NA1011" "001111" "000101" "1NA1111" "NANA0NA0NA"
[92] "001011" "0NA1NANANA" "011000" "000010" "NANA00NANA" "1NANA000" "001110"
[99] "NA1NANANANA" "NANANA11NA" "NANA1010" "000NA01" "001NA00" "1111NANA" "NANANA1NANA"
[106] "NANANANANA1" "NANANANA01" "NANA001NA" "011001" "111NANANA" "NANANA00NA" "011100"
[113] "0NA0110" "0NA01NANA" "NANA01NANA" "0NA001NA" "1NA1100" "1000NANA" "0NANANANA1"
[120] "NANANA0NANA" "00NA001" "0NA100NA" "NA1101NA" "01NA11NA" "00NA01NA" "01000NA"
[127] "010001" "NA00000" "100111" "001010" "0010NA0" "NANA1011" "000110"
[134] "1NA1101" "010NANANA" "1NA10NANA" "0NANANA01" "NA0NA1NANA" "1NANANA00" "00110NA"
[141] "NANA11NANA" "NA0NANANANA" "00NA011" "0NANA011" "001NA1NA" "NANA0000" "000NANA1"
[148] "NA1111NA" "0NA011NA" "1NA001NA" "0001NA0" "0NANA111" "0NANA100" "1NANA111"
[155] "001NANA1" "NA0NANA11" "011NA0NA" "011NA11" "000NA11" "01NA000" "NA01000"
[162] "NA11000" "1NA0000" "100000" "1NA1NA00" "111000" "NA11111" "NANANA110"
[169] "NANANA111" "NANANA100" "NANANA01NA" "NANANA101" "NANANA10NA" # some of the spouse memory disease patterns are suspect or look like they should not have been included.
spouse_memory_disease_patterns_to_examine <- c(
"000000"
,"NANANANANANA"
,"001000"
,"0000NANA"
,"NANA1000"
,"NANA1NANANA"
,"00NANANANA"
,"000100"
,"001NANANA"
,"00100NA"
,"NA1NANA0NA"
,"0010NANA"
,"00010NA"
,"NA0000NA"
,"01NANA00"
,"NANA10NANA"
,"NANA100NA"
,"001NA0NA"
,"0NANANANANA"
,"NANANANA1NA"
,"011110"
,"010000"
,"1NA100NA"
,"00101NA"
,"101111"
,"001001"
,"10NANANANA"
,"0000NA0"
,"NANANANA00"
,"NANANANANA0"
,"111011"
,"0NANANA10"
,"NANA10NA1"
,"NA00011"
,"NA0100NA"
,"01100NA"
,"000NANANA"
,"0110NANA"
,"1NA0111"
,"00000NA"
,"NA000NANA"
,"1NA1011"
,"000101"
,"NANA0NA0NA"
,"001011"
,"011000"
,"000010"
,"NANA00NANA"
,"1NANA000"
,"001110"
,"NANA1010"
,"001NA00"
,"011001"
,"NANANA00NA"
,"011100"
,"0NA0110"
,"1NA1100"
,"1000NANA"
,"NANANA0NANA"
,"0NA100NA"
,"NA1101NA"
,"01000NA"
,"010001"
,"NA00000"
,"100111"
,"001010"
,"0010NA0"
,"NANA1011"
,"000110"
,"1NA1101"
,"010NANANA"
,"1NA10NANA"
,"1NANANA00"
,"00110NA"
,"NA0NANANANA"
,"NANA0000"
,"1NA001NA"
,"0001NA0"
,"0NANA100"
,"011NA0NA"
,"01NA000"
,"NA01000"
,"NA11000"
,"1NA0000"
,"100000"
,"1NA1NA00"
,"111000"
,"NANANA110"
,"NANANA100"
,"NANANA101"
,"NANANA10NA"
)
d <- ds %>%
select(
id, year,shhidpnr, age_at_visit, date_at_visit, spouse_memory_disease
,smemry, smemryq, smemrye, smemryf, salzhe, salzheq, salzhee, salzhflag
,sdemen, sdemenq,sdemene,sdemenflag
,spouse_dementia_ever
,spouse_alzheimer_ever
,spouse_memoryproblems_ever
,spouse_memory_disease_ever
,spouse_memory_disease
,spouse_memory_disease_pattern
) %>%
filter(spouse_memory_disease_pattern %in% spouse_memory_disease_patterns_to_examine)
#Number of individuals with a suspect pattern of reported spouse memory disease
length(unique(d$id))[1] 388length(unique(d$shhidpnr))[1] 407# Exclude those with spouses who likely do not have dementia or AD
ds <- ds %>% dplyr::filter(!spouse_memory_disease_pattern %in% spouse_memory_disease_patterns_to_examine)
#Number of individuals once suspect patterns are excluded
length(unique(ds$id))[1] 771length(unique(ds$shhidpnr))[1] 782#Excluding those whose spouses likely do not have dementia (i.e., suspect memory disease patterns) what
# is the frequency of each pattern of diagnosis for the spouse?
table(ds$spouse_memory_disease_pattern)
000001 000011 00001NA 0000NA1 000111 00011NA 0001NANA 000NA01 000NA11 000NA1NA
510 330 198 30 156 192 228 18 12 12
000NANA1 001111 00111NA 0011NANA 001NA11 001NA1NA 001NANA1 00NA001 00NA011 00NA01NA
6 72 102 114 6 12 6 6 6 6
00NA11NA 00NA1NANA 00NANA01 00NANA1NA 00NANANA1 011111 01111NA 0111NANA 011NA11 011NANANA
6 24 12 6 6 54 42 72 6 156
01NA11NA 01NANANANA 0NA001NA 0NA011NA 0NA01NANA 0NA1NANANA 0NANA011 0NANA111 0NANA1NANA 0NANANA01
6 306 18 6 6 12 6 12 18 6
0NANANANA1 1111NANA 111NANANA 11NANANANA 1NA1000 1NA1111 1NA111NA 1NA11NANA 1NA1NANANA 1NANA111
6 6 18 66 174 30 30 54 114 6
1NANANANANA NA00001 NA00111 NA0011NA NA001NANA NA00NA1NA NA0111NA NA011NANA NA01NANANA NA0NA1NANA
480 24 12 12 6 6 6 12 42 6
NA0NANA11 NA11111 NA1111NA NA111NANA NA11NANANA NA1NANANANA NANA001NA NANA011NA NANA01NANA NANA1111
6 6 6 6 30 84 6 24 12 54
NANA111NA NANA11NANA NANANA001 NANANA011 NANANA01NA NANANA0NA1 NANANA111 NANANA11NA NANANA1NANA NANANANA01
12 18 114 90 6 36 78 30 24 6
NANANANA11 NANANANANA1
30 36 #Display the flag variable patterns
table(ds$smemryf,ds$year)
2004 2006 2008 2010 2012 2014
0 655 614 513 0 0 0
1 2 1 1 0 0 0
2 2 2 2 0 0 0
3 0 0 1 0 0 0table(ds$salzhflag, ds$year)
2004 2006 2008 2010 2012 2014
0 0 0 0 0 423 322
1 0 0 0 0 1 2
3 0 0 0 0 1 4
6 0 0 0 0 5 0
8 0 0 0 0 1 0table(ds$sdemenflag, ds$year)
2004 2006 2008 2010 2012 2014
0 0 0 0 0 416 321
2 0 0 0 0 1 0
3 0 0 0 0 5 5
6 0 0 0 0 8 0
8 0 0 0 0 1 2#Are there any individuals where the spouse has changed?
test <- ds %>%
dplyr::group_by(id) %>%
dplyr::mutate(
firstspouse = dplyr::first(shhidpnr),
flag = ifelse(all(shhidpnr== firstspouse | shhidpnr==0, na.rm = T), 0, 1)
)
test %>%
select(id, year,shhidpnr,flag, spouse_memory_disease_pattern) %>%
dplyr::filter(flag==1)Source: local data frame [114 x 5]
Groups: id [19]
id year shhidpnr flag spouse_memory_disease_pattern
<dbl> <fctr> <dbl> <dbl> <chr>
1 16935010 2004 0 1 NA0011NA
2 16935010 2006 16935011 1 NA0011NA
3 16935010 2008 16935011 1 NA0011NA
4 16935010 2010 16935011 1 NA0011NA
5 16935010 2012 16935011 1 NA0011NA
6 16935010 2014 0 1 NA0011NA
7 18212010 2004 0 1 NANA1111
8 18212010 2006 0 1 NANA1111
9 18212010 2008 18212011 1 NANA1111
10 18212010 2010 18212011 1 NANA1111
# ... with 104 more rows#What does data look like for variables that do not change with time?
ds %>% select_(.dots = variables_static) # A tibble: 4,626 × 9
id male birth_year birth_month race hispanic cohort edu_years highest_degree
<dbl> <fctr> <dbl> <dbl> <fctr> <dbl> <fctr> <dbl> <dbl>
1 3020 Woman 1938 9 White 0 Hrs 16 5
2 3020 Woman 1938 9 White 0 Hrs 16 5
3 3020 Woman 1938 9 White 0 Hrs 16 5
4 3020 Woman 1938 9 White 0 Hrs 16 5
5 3020 Woman 1938 9 White 0 Hrs 16 5
6 3020 Woman 1938 9 White 0 Hrs 16 5
7 10281020 Man 1934 8 Black 0 Hrs 11 0
8 10281020 Man 1934 8 Black 0 Hrs 11 0
9 10281020 Man 1934 8 Black 0 Hrs 11 0
10 10281020 Man 1934 8 Black 0 Hrs 11 0
# ... with 4,616 more rows#How many distinct values are there for each static variable?
set.seed(42)
ds %>%
select_(.dots = variables_static) %>%
# filter(id %in% sample(unique(id),100)) %>%
summarize_all(n_distinct) %>%
t() [,1]
id 771
male 2
birth_year 61
birth_month 12
race 4
hispanic 2
cohort 7
edu_years 19
highest_degree 8#How many distinct values are there for variables that change over time?
ds %>%
select_(.dots = variables_longitudinal) %>%
summarize_all(n_distinct) %>%
t() [,1]
lb_wave 5
year 6
lb_65_wave 5
hrs_tscore 45
interview_date 84
responded 2
proxy 3
hhres 10
countb20r 5
shhidpnr 782
rmaritalst 8
intage_r 68
rpartst 3
score_loneliness_3 11
score_loneliness_11 45
snspouse 4
snchild 4
snfamily 4
snfriends 4
socialnetwork_total 6
close_social_network 37
social_support_mean 47
social_strain_mean 159
social_contact_total 51
activity_mean 70
activity_sum 70
srmemory 7
srmemoryp 5
wrectoti 13
wrectotd 13
listassi 6
mentalstatus_tot 13
vocab_total 14
dep_total 12
healthcond 10
exercise 16
memry 6
memryq 9
smemry 5
smemryq 10
memrye 4
smemrye 4
memryf 5
smemryf 6
alzhe 5
salzhe 6
alzheq 9
salzheq 9
alzhee 4
salzhee 4
alzhflag 6
salzhflag 7
demen 5
sdemen 6
demenq 9
sdemenq 9
demene 4
sdemene 4
demenflag 8
sdemenflag 7
dementia_ever 3
alzheimer_ever 3
memoryproblems_ever 3
memory_disease_ever 3
spouse_dementia_ever 3
spouse_alzheimer_ever 3
spouse_memoryproblems_ever 3
spouse_memory_disease_ever 1
spouse_memory_disease 3
spouse_memory_disease_pattern 82Development
This section will contain a close up examination of relevant variables, one by one.
Static
This section focuses on variables with values that do not change with time.
id
# How many respondents are in the sample?
ds %>% distinct(id) %>% count()# A tibble: 1 × 1
n
<int>
1 771ds %>% group_by(id) %>% summarize(n=n())# A tibble: 771 × 2
id n
<dbl> <int>
1 3020 6
2 10281020 6
3 10397010 6
4 10989020 6
5 11071020 6
6 11379020 6
7 11626011 6
8 11671040 6
9 12276030 6
10 12292010 6
# ... with 761 more rowsmale
# what is the gender composion of the sample?
ds %>% group_by(male) %>% summarize(n=n())# A tibble: 2 × 2
male n
<fctr> <int>
1 Man 1890
2 Woman 2736ds %>% histogram_discrete("male")
# what is gender composition over time?
ds %>% count_over_time("year","male")
ds %>% count_over_time("lb_wave","male")Warning: Removed 2 rows containing missing values (position_stack).Warning: Removed 2 rows containing missing values (geom_text).
race
# what is the race compositon of the sample
ds %>% group_by(male) %>% summarize(n=n()) %>% neat("pandoc")| male | n |
|---|---|
| Man | 1890 |
| Woman | 2736 |
ds %>% histogram_discrete("race")
# what is race composition over time?
ds %>% count_over_time("year","race")
ds %>% count_over_time("lb_wave","race")Warning: Removed 4 rows containing missing values (position_stack).Warning: Removed 4 rows containing missing values (geom_text).
# there may not be enough sample size if split by race
ds %>%
dplyr::filter(lb_wave == 4) %>%
group_by(race) %>%
distinct(id ) %>% count()# A tibble: 3 × 2
race n
<fctr> <int>
1 White 35
2 Black 3
3 Other 3Dynamic
lb Wave
Mental Status
# examine mental status over time
ds %>% over_time("year", "mentalstatus_tot")Measure : mentalstatus_tot
2004 2006 2008 2010 2012 2014 <NA>
0 . . . . . 1 .
1 . . 1 . . 1 .
2 . 1 1 3 2 1 .
3 2 2 3 3 4 4 .
4 1 3 3 5 5 5 .
5 2 5 7 6 13 8 .
6 17 15 13 28 29 15 .
7 45 47 45 67 61 72 .
8 131 125 121 184 138 121 .
9 318 304 300 207 184 160 .
NaN 162 156 126 141 162 146 .
<NA> 93 113 151 127 173 237 . year mean sd count
1 2004 8.42 0.905 516
2 2006 8.37 1.021 502
3 2008 8.34 1.113 494
4 2010 8.03 1.183 503
5 2012 7.94 1.294 436
6 2014 7.93 1.336 388ds %>% over_time("lb_wave", "mentalstatus_tot")Measure : mentalstatus_tot
1 2 3 4 <NA>
0 . . . . 1
1 . . 1 . 1
2 . 1 . . 7
3 1 2 . . 15
4 4 1 2 . 15
5 3 4 1 1 32
6 14 18 3 1 81
7 37 47 29 5 219
8 139 95 60 7 519
9 299 207 84 19 864
NaN 129 78 31 8 647
<NA> 4 1 . . 889 lb_wave mean sd count
1 1 8.41 0.925 497
2 2 8.25 1.086 375
3 3 8.17 1.082 180
4 4 8.27 1.039 33set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, mentalstatus_tot
) %>%
filter(id %in% sample(unique(id),100))
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "mentalstatus_tot",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 0Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 0Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Word List Recall
# examine the assignment of word lists over time
ds %>% over_time("year", "listassi")Measure : listassi
2004 2006 2008 2010 2012 2014 <NA>
1 171 180 136 157 126 160 .
11 141 187 166 125 135 138 .
21 167 160 135 108 169 111 .
31 151 133 152 189 119 68 .
NaN 50 . 34 67 53 58 .
<NA> 91 111 148 125 169 236 . year mean sd count
1 2004 15.73 11.285 630
2 2006 14.73 10.882 660
3 2008 16.14 11.090 589
4 2010 16.68 12.013 579
5 2012 16.12 10.698 549
6 2014 12.82 10.523 477ds %>% over_time("lb_wave", "listassi")Measure : listassi
1 2 3 4 <NA>
1 149 129 63 18 571
11 154 108 43 11 576
21 161 102 50 7 530
31 149 106 49 4 504
NaN 17 9 6 1 229
<NA> . . . . 880 lb_wave mean sd count
1 1 16.06 11.065 613
2 2 15.16 11.411 445
3 3 15.15 11.584 205
4 4 10.25 10.225 40set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, wrectoti, wrectotd, listassi
) %>%
filter(id %in% sample(unique(id),100))
# A single, elemental graph
d %>% elemental_line(
variable_name = "wrectoti",
time_metric = "age_at_visit",
color_name = "black",
line_alpha = .5,
line_size = 1,
smoothed = T
)
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "wrectoti",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 1.7177e-31Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 1.7177e-31Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "wrectotd",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 1.7177e-31Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 1.7177e-31Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Social Strain
ds %>% summarize_over_time("year", "social_strain_mean") year mean sd count
1 2006 5.53 1.433 277
2 2008 5.46 1.645 256
3 2010 5.43 1.752 255
4 2012 5.29 1.715 211
5 2014 5.12 1.598 209ds %>% summarize_over_time("lb_wave", "social_strain_mean") lb_wave mean sd count
1 1 5.55 1.606 504
2 2 5.37 1.669 453
3 3 5.09 1.563 210
4 4 4.98 1.612 41set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, social_strain_mean
) %>%
filter(id %in% sample(unique(id),100))
# A single, elemental graph
d %>% elemental_line(
variable_name = "social_strain_mean",
time_metric = "age_at_visit",
color_name = "black",
line_alpha = .5,
line_size = 1,
smoothed = T
)
# assemble various sinle graphs in a integrated information display
d %>% complex_line(
variable_name = "social_strain_mean",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 6.1317e-31Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 6.1317e-31Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Loneliness Three Item Short
ds %>% summarize_over_time("year", "score_loneliness_3") year mean sd count
1 2004 1.42 0.494 117
2 2006 1.44 0.489 275
3 2008 1.55 0.556 253
4 2010 1.51 0.589 253
5 2012 1.56 0.548 211
6 2014 1.61 0.593 206ds %>% summarize_over_time("lb_wave", "score_loneliness_3") lb_wave mean sd count
1 1 1.51 0.543 617
2 2 1.54 0.566 449
3 3 1.52 0.558 208
4 4 1.44 0.482 41set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, score_loneliness_3
) %>%
filter(id %in% sample(unique(id),100))
# A single, elemental graph
d %>% elemental_line(
variable_name = "score_loneliness_3",
time_metric = "age_at_visit",
color_name = "black",
line_alpha = .5,
line_size = 1,
smoothed = T
)
# assemble various sinle graphs in a integrated information display
d %>% complex_line(
variable_name = "score_loneliness_3",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 6.9377e-31Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 6.9377e-31Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Loneliness Full Scale
# examine the assignment of word lists over time
ds %>% summarize_over_time("year", "score_loneliness_11") year mean sd count
1 2008 1.56 0.425 254
2 2010 1.52 0.409 253
3 2012 1.58 0.404 210
4 2014 1.60 0.441 206ds %>% summarize_over_time("lb_wave", "score_loneliness_11") lb_wave mean sd count
1 1 1.61 0.448 285
2 2 1.56 0.408 389
3 3 1.51 0.409 208
4 4 1.49 0.361 41set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, score_loneliness_11
) %>%
filter(id %in% sample(unique(id),100))
# assemble various sinle graphs in a integrated information display
d %>% complex_line(
variable_name = "score_loneliness_11",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 2.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 6.2287e-16Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 2.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 6.2287e-16Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Social Network
ds %>% summarize_over_time("year", "socialnetwork_total") year mean sd count
1 2004 3.61 0.698 122
2 2006 3.70 0.553 278
3 2008 3.55 0.679 256
4 2010 3.45 0.719 255
5 2012 3.21 0.916 210
6 2014 3.10 0.827 209ds %>% summarize_over_time("lb_wave", "socialnetwork_total") lb_wave mean sd count
1 1 3.57 0.701 628
2 2 3.39 0.810 451
3 3 3.22 0.778 210
4 4 3.22 0.652 41set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, socialnetwork_total
) %>%
filter(id %in% sample(unique(id),100))
# assemble various sinle graphs in a integrated information display
d %>% complex_line(
variable_name = "socialnetwork_total",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 1.4683e-30Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 1.4683e-30Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Close Social Network
# examine the assignment of word lists over time
ds %>% summarize_over_time("year", "close_social_network") year mean sd count
1 2004 1.67 4.665 771
2 2006 3.44 5.778 771
3 2008 3.32 6.049 771
4 2010 2.88 5.127 771
5 2012 2.56 5.316 771
6 2014 2.41 4.798 771ds %>% summarize_over_time("lb_wave", "close_social_network") lb_wave mean sd count
1 1 9.56 6.131 630
2 2 9.24 6.164 454
3 3 9.19 5.309 211
4 4 9.71 5.711 41set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, close_social_network
) %>%
filter(id %in% sample(unique(id),100))
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "close_social_network",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 1.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 1.4683e-30Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 1Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 1.4683e-30Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 1
Activity
# examine the assignment of activity over time
ds %>% summarize_over_time("year", "activity_sum") year mean sd count
1 2010 54.26 12.290 225
2 2012 54.17 12.408 167
3 2014 53.52 12.588 174ds %>% summarize_over_time("lb_wave", "activity_sum") lb_wave mean sd count
1 1 52.35 11.975 66
2 2 53.88 12.507 288
3 3 54.66 12.323 178
4 4 54.91 12.880 34set.seed(42)
# ids_1000 <- sample(unique(ds$id),
d <- ds %>%
mutate(
age_at_visit = intage_r,
date_at_visit = interview_date
) %>%
select(
id, year, lb_wave, age_at_visit, date_at_visit, activity_sum
) %>%
filter(id %in% sample(unique(id),100))
# assemble various single graphs in a integrated information display
d %>% complex_line(
variable_name = "activity_sum",
line_size = 1,
line_alpha = .5
)Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : pseudoinverse used at 0.985Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : neighborhood radius 2.015Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : reciprocal condition number 4.3812e-16Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric, : There are other near singularities as
well. 4.0602Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 0.985Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 2.015Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition number 4.3812e-16Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x else if (is.data.frame(newdata))
as.matrix(model.frame(delete.response(terms(object)), : There are other near singularities as well. 4.0602
srmemory
ds %>% over_time("year","srmemory")Measure : srmemory
2004 2006 2008 2010 2012 2014 <NA>
1 25 32 32 32 33 24 .
2 145 143 123 139 150 124 .
3 288 276 275 263 225 217 .
4 138 137 133 121 110 97 .
5 34 32 26 26 28 15 .
NaN 50 39 34 65 54 58 .
<NA> 91 112 148 125 171 236 . year mean sd count
1 2004 3.02 0.908 630
2 2006 2.99 0.930 620
3 2008 3.00 0.911 589
4 2010 2.95 0.920 581
5 2012 2.91 0.957 546
6 2014 2.91 0.885 477ds %>% over_time("lb_wave", "srmemory")Measure : srmemory
1 2 3 4 <NA>
1 30 21 10 . 117
2 135 114 55 12 508
3 280 200 92 22 950
4 137 87 41 6 465
5 29 23 7 . 102
NaN 19 9 6 1 265
<NA> . . . . 883 lb_wave mean sd count
1 1 3.00 0.913 611
2 2 2.95 0.920 445
3 3 2.90 0.891 205
4 4 2.85 0.662 40
Social Support